The present invention relates to model based decision support, and more particularly, to decision support using organ models estimated from medical image data and learning based discriminative distance functions.
Valvular heart disease (VHD) is a cardiac disorder that affects a large number of patients and often requires elaborate diagnostic procedures, intervention, and long-term management. Abnormalities may occur in conjunction with other heart diseases, and can be caused by congenital defects, pulmonary hypertension, endocarditis, rheumatic fever, and carcinoid heart disease. Such conditions require constant monitoring and a complex clinical workflow, including patient evaluation, percutaneous intervention planning, valve replacement and repair, and follow-up evaluations.
Treatment of VHD is typically expensive and conventional VHD treatment has a relatively high in-hospital death rate due to elaborate, time consuming, and potentially inaccurate diagnostic procedures and complex interventions into patients' cardiac systems. Recent advances in medical imaging technology have enabled 4D imaging with computed tomography (CT) and ultrasound. However, due to lack of efficient and convenient tools, anatomical performance assessment of the cardiac valves typically relies on manual measurements in 2D image planes derived from the 4D image acquisitions. Although such performance assessment can be error prone and time consuming, diagnosis, treatment decisions, interventional planning, and follow up evaluation typically rely on such performance assessment, which can lead to suboptimal treatment results, follow up interventions, and increased treatment costs. Moreover, clinical decisions are based on generic information from clinical guidelines and publications and personal experience of clinicians. Clinical decisions are not necessarily personalized to the specific patient, due to the potential lack of similar cases, which would provide patient histories and treatment results as references for decision support.